Alerting on Driving Affecting Signal

ABSTRACT

A method for providing an in-vehicle indication, the method may include obtaining, during a driving session of a vehicle, external audio information about audio generated outside the vehicle; determining whether the audio generated outside the vehicle comprises driving affecting audio; and generating the in-vehicle indication, within the vehicle, when determining that the audio generated outside comprises driving affecting audio. The in-vehicle indication is indicative about the driving affecting audio generated outside the vehicle.

BACKGROUND

Playing loud music in a car, especially while driving may distract thedriver and may increase the chances of a car accident. It is evenillegal in various countries.

There is a need to reduce the risk associated with playing loud music ina car.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIG. 1A illustrates an example of a method;

FIG. 1B illustrates an example of a signature;

FIG. 1C illustrates an example of a dimension expansion process;

FIG. 1D illustrates an example of a merge operation;

FIG. 1E illustrates an example of hybrid process;

FIG. 1F illustrates an example of a method;

FIG. 1G illustrates an example of a method;

FIG. 1H illustrates an example of a method;

FIG. 1I illustrates an example of a method;

FIG. 1J illustrates an example of a method;

FIG. 1K illustrates an example of a method;

FIG. 1L illustrates an example of a method;

FIG. 1M illustrates an example of a system;

FIG. 1N is a partly-pictorial, partly-block diagram illustration of anexemplary obstacle detection and mapping system, constructed andoperative in accordance with embodiments described herein;

FIG. 2A is an example of a situation;

FIG. 2B is an example of a situation;

FIG. 3A is an example of a vehicle;

FIG. 3B is an example of a vehicle;

FIG. 3C is an example of a vehicle;

FIG. 4A is an example of a method;

FIG. 4B is an example of a method; and

FIG. 4C is an example of a method.

DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresent invention may be practiced without these specific details. Inother instances, well-known methods, procedures, and components have notbeen described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed outand distinctly claimed in the concluding portion of the specification.The invention, however, both as to organization and method of operation,together with objects, features, and advantages thereof, may best beunderstood by reference to the following detailed description when readwith the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration,elements shown in the figures have not necessarily been drawn to scale.For example, the dimensions of some of the elements may be exaggeratedrelative to other elements for clarity. Further, where consideredappropriate, reference numerals may be repeated among the figures toindicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for themost part, be implemented using electronic components and circuits knownto those skilled in the art, details will not be explained in anygreater extent than that considered necessary as illustrated above, forthe understanding and appreciation of the underlying concepts of thepresent invention and in order not to obfuscate or distract from theteachings of the present invention.

Any reference in the specification to a method should be applied mutatismutandis to a device or computerized system capable of executing themethod and/or to a non-transitory computer readable medium that storesinstructions for executing the method.

Any reference in the specification to a computerized system or deviceshould be applied mutatis mutandis to a method that may be executed bythe computerized system, and/or may be applied mutatis mutandis tonon-transitory computer readable medium that stores instructionsexecutable by the computerized system.

Any reference in the specification to a non-transitory computer readablemedium should be applied mutatis mutandis to a device or computerizedsystem capable of executing instructions stored in the non-transitorycomputer readable medium and/or may be applied mutatis mutandis to amethod for executing the instructions.

Any combination of any module or unit listed in any of the figures, anypart of the specification and/or any claims may be provided.

The specification and/or drawings may refer to an image. An image is anexample of a media unit. Any reference to an image may be appliedmutatis mutandis to a media unit. A media unit may be an example ofsensed information unit. Any reference to a media unit may be appliedmutatis mutandis to sensed information. The sensed information may besensed by any type of sensors—such as a visual light camera, or a sensorthat may sense infrared, radar imagery, ultrasound, electro-optics,radiography, LIDAR (light detection and ranging), etc.

The specification and/or drawings may refer to a processor. Theprocessor may be a processing circuitry. The processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in thespecification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of computerized systems, units, components, processors,sensors, illustrated in the specification and/or drawings may beprovided.

Any reference to any of the term “comprising” may be applied mutatismutandis to the terms “consisting” and “consisting essentially of”.

Any reference to any of the term “consisting” may be applied mutatismutandis to the terms “comprising” and “consisting essentially of”.

Any reference to any of the term “consisting essentially of” may beapplied mutatis mutandis to the terms “comprising” and “comprising”.

Audio generated outside the vehicle may include driving affecting audio.Driving affecting audio is audio that may affect a manner in which avehicle is driven. Driving affecting audio may be for example, any audiogenerated by an entity (being human or non-human) that may affect thedriving of the vehicle. Affect may include should be taken into accountwhen driving. Non-limiting example are audio alerts, audio that wasfound (for example learnt) as preceding accidents or near accidents,sounds of brakes, horns, shouts, a sound of an approaching train, andthe like. The driving affecting audio may be aimed to a driver (forexample—shouts, alerts, horns) or not directly aimed to the driver(braking sounds), and the like. What amounts to a driving affectingaudio may be learnt in any manner, may be provided to the systemexecuting any of the following methods in any manner, may change overtime, may be determined based on feedback, and the like.

FIGS. 2A and 2B illustrate examples in which vehicle 51 is in situationsthat may cause other entities outside the vehicle to generate audio thatmay include driving affecting audio (denoted 59).

In FIG. 2A, vehicle 51 reaches a zebra cross located at excessivespeed—which may trigger a generation of driving affecting audio 59 by aperson that crosses the zebra crossing, one or more other pedestrian, avehicle 52 following vehicle 51 and/or another vehicle 53 that drives atan opposite lane of vehicle 51.

In FIG. 2B, vehicle 51 deviates to an opposite lane and this may ageneration of driving affecting audio 59 by another vehicle 53 thatdrives at an opposite lane of vehicle 51.

FIGS. 3A, 3B and 3C illustrates examples of vehicle 51 and drivingaffecting audio 59 generated outside the vehicle. Driver 58 is locatedwithin the vehicle.

The vehicle includes sensing unit 60 that may include one or more audiosensors (in FIG. 3C the sensing unit includes audio sensors 61 forsensing audio generated outside the vehicle and a in-vehicle sensor 62for sensing the audio near driver 58). Any of the sensing unit 61 mayinclude any other type of sensors.

The vehicle also includes a decision unit 70 that may include aprocessor and/or any other components, that may be configured todetermine whether the audio generated outside the vehicle includesdriving affecting audio.

The vehicle may include generator 80 for generating at least one of thein vehicle indicator (denoted 69 in FIGS. 3A and 3C) or for generating arequest, a trigger or a command (denoted 69′ in FIG. 3B) to anotherdevice (such a driver wearable device 57) to generate the in-vehicleindication.

FIG. 4A illustrates method 101 for providing an in-vehicle indication.

Method 100 start by step 110 of obtaining, during a driving session of avehicle, external audio information about audio generated outside thevehicle.

Step 110 may include sensing the audio generated outside the vehicle andgenerating the external audio information and/or receiving sensedinformation indicative of the audio generated outside the vehicle, andthe like. Step 110 may be executed by one or more audio sensors locatedin any location—for example—external audio sensors facing the exteriorof the vehicle, or acoustically coupled to the exterior of the vehicle.

Step 110 may be followed by step 120 of determining whether the audiogenerated outside the vehicle includes driving affecting audio. Thedetermination can be made in any manner, for example searching forsignatures of driving affecting audio, searching in any manner thatpresence of driving affecting audio, applying a machine learning processtrained to locate the driving affecting audio, and the like. An examplefor detecting certain media unit content is illustrated in PCT patentapplication PCT/IB2019/058207 which is incorporated herein by reference.

Step 120 may be followed by step 130 of generating the in-vehicleindication, within the vehicle, when determining that the audiogenerated outside comprises driving affecting audio. The in-vehicleindication is indicative about the driving affecting audio generatedoutside the vehicle.

The in-vehicle indication may be provided in any form—audio, visual,audio-visual, tactile, or any other signal. Tactile alerts may beprovided by contacting the driver in a controlled manner.

The driving relevant in-vehicle audio may be reconstructed version ofthe driving affecting audio. Reconstructed—may include audio componentsof the driving affecting audio, or may be processed in any othermanner—for example by applying filtering, noise reduction, and the like.

Step 130 may take into account various parameters—such as other signalsthat may be presented to the driver (for example—other sounds that areheard by the driver, the manner in which the driver is receiving othersignals—using earphones, using a vehicle media device, the loudnessother audio signals heard by the driver, and the like).

Step 130 may include step 131 of estimating at least one other sound(for example—music, podcast, radio station) to be generated within thevehicle, when the driving relevant in-vehicle audio is played in thevehicle, wherein the at least one other sound differs from the drivingrelevant in-vehicle audio.

Step 130 may include step 132 of generating the driving relevantin-vehicle audio to be heard by at least the driver at the presence ofthe at least one other sound. Thus—the in-vehicle audio should overcomethe at least one other sound.

FIG. 4B illustrates method 102 for providing an in-vehicle indication.

Method 101 start by step 110 of obtaining, during a driving session of avehicle, external audio information about audio generated outside thevehicle.

Step 110 may be followed by step 120 of determining whether the audiogenerated outside the vehicle includes driving affecting audio.

Step 120 may be followed by step 134 of providing a request, a triggeror a command to another device to generate the in-vehicle indication.

The request, trigger a command can be sent to another device that inturn may generate the in-vehicle indication.

The other device may be a part of the vehicle, a multimedia device, aspeaker of the vehicle, the other device may be positioned (at leasttemporarily) within the vehicle, may belong to or used by the driver(for example earphones, mobile phone, mobile computer, smart phone), andthe like.

Step 134 may also include requesting, commanding or triggering the otherdevice to change at least one other signal outputted by the other devicewhen the in-vehicle indication is generated or in timing proximity (forexample a few seconds) to the transmission of the in-vehicle indication.

For example—the other device may be requested, triggered to orinstructed to generate only the in-vehicle indication, to attenuateother output signals when the in-vehicle indication is outputted, andthe like.

FIG. 4C illustrates method 103 for providing an in-vehicle indication.

Method 103 start by step 110 of obtaining, during a driving session of avehicle, external audio information about audio generated outside thevehicle.

Step 110 may be followed by step 120 of determining whether the audiogenerated outside the vehicle includes driving affecting audio.

Step 120 may be followed by step 140 of determining whether to generatethe in-vehicle indication.

Step 140 may include determining not to generate the in-vehicleindication based on a relevancy of the generated in-vehicle indication.

For example—step 140 may determine not to generate the in-vehicleindication when the driving affecting audio is heard (or at leastsufficiently heard) by the driver. What amounts to sufficiently heardcan be determined in any manner—for example be of at least a predefinedvolume, exhibit at least a predefined signal to noise ratio, beingnoticed by the driver, and the like).

Yet for another example—step 140 may determine not to generate thein-vehicle indication when the vehicle is driving in an autonomous mode.

Step 140 may be followed by step 130 when determining to generate thein-vehicle indication. Step 140 may be followed by step 134 of FIG. 4C,and the like.

Low Power Generation of Signatures

The analysis of content of a media unit may be executed by generating asignature of the media unit and by comparing the signature to referencesignatures. The reference signatures may be arranged in one or moreconcept structures or may be arranged in any other manner. Thesignatures may be used for object detection or for any other use.

The signature may be generated by creating a multidimensionalrepresentation of the media unit. The multidimensional representation ofthe media unit may have a very large number of dimensions. The highnumber of dimensions may guarantee that the multidimensionalrepresentation of different media units that include different objectsis sparse—and that object identifiers of different objects are distantfrom each other—thus improving the robustness of the signatures.

The generation of the signature is executed in an iterative manner thatincludes multiple iterations, each iteration may include an expansionoperations that is followed by a merge operation. The expansionoperation of an iteration is performed by spanning elements of thatiteration. By determining, per iteration, which spanning elements (ofthat iteration) are relevant—and reducing the power consumption ofirrelevant spanning elements—a significant amount of power may be saved.

In many cases, most of the spanning elements of an iteration areirrelevant—thus after determining (by the spanning elements) theirrelevancy—the spanning elements that are deemed to be irrelevant may beshut down a/or enter an idle mode.

FIG. 1A illustrates a method 5000 for generating a signature of a mediaunit.

Method 5000 may start by step 5010 of receiving or generating sensedinformation.

The sensed information may be a media unit of multiple objects.

Step 5010 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may include:

Step 5020 of performing a k'th iteration expansion process (k may be avariable that is used to track the number of iterations).

Step 5030 of performing a k'th iteration merge process.

Step 5040 of changing the value of k.

Step 5050 of checking if all required iterations were done—if soproceeding to step 5060 of completing the generation of the signature.Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion results 5120.

The output of step 5030 is a k'th iteration merge results 5130.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

At least some of the K iterations involve selectively reducing the powerconsumption of some spanning elements (during step 5020) that are deemedto be irrelevant.

FIG. 1B is an example of an image signature 6027 of a media unit that isan image 6000 and of an outcome 6013 of the last (K'th) iteration.

The image 6001 is virtually segments to segments 6000(i,k). The segmentsmay be of the same shape and size but this is not necessarily so.

Outcome 6013 may be a tensor that includes a vector of values per eachsegment of the media unit. One or more objects may appear in a certainsegment. For each object—an object identifier (of the signature) pointsto locations of significant values, within a certain vector associatedwith the certain segment.

For example—a top left segment (6001(1,1)) of the image may berepresented in the outcome 6013 by a vector V(1,1) 6017(1,1) that hasmultiple values. The number of values per vector may exceed 100, 200,500, 1000, and the like.

The significant values (for example—more than 10, 20, 30, 40 values,and/or more than 0.1%, 0.2%. 0.5%, 1%, 5% of all values of the vectorand the like) may be selected. The significant values may have thevalues—but may be selected in any other manner.

FIG. 1B illustrates a set of significant responses 6015(1,1) of vectorV(1,1) 6017(1,1). The set includes five significant values (such asfirst significant value SV1(1,1) 6013(1,1,1), second significant valueSV2(1,1), third significant value SV3(1,1), fourth significant valueSV4(1,1), and fifth significant value SV5(1,1) 6013(1,1,5).

The image signature 6027 includes five indexes for the retrieval of thefive significant values—first till fifth identifiers ID1-ID5 are indexesfor retrieving the first till fifth significant values.

FIG. 1C illustrates a k'th iteration expansion process.

The k'th iteration expansion process start by receiving the mergeresults 5060′ of a previous iteration.

The merge results of a previous iteration may include values areindicative of previous expansion processes—for example—may includevalues that are indicative of relevant spanning elements from a previousexpansion operation, values indicative of relevant regions of interestin a multidimensional representation of the merge results of a previousiteration.

The merge results (of the previous iteration) are fed to spanningelements such as spanning elements 5061(1)-5061(J).

Each spanning element is associated with a unique set of values. The setmay include one or more values. The spanning elements apply differentfunctions that may be orthogonal to each other. Using non-orthogonalfunctions may increase the number of spanning elements—but thisincrement may be tolerable.

The spanning elements may apply functions that are decorrelated to eachother—even if not orthogonal to each other.

The spanning elements may be associated with different combinations ofobject identifiers that may “cover” multiple possible media units.Candidates for combinations of object identifiers may be selected invarious manners—for example based on their occurrence in various images(such as test images) randomly, pseudo randomly, according to some rulesand the like. Out of these candidates the combinations may be selectedto be decorrelated, to cover said multiple possible media units and/orin a manner that certain objects are mapped to the same spanningelements.

Each spanning element compares the values of the merge results to theunique set (associated with the spanning element) and if there is amatch—then the spanning element is deemed to be relevant. If so—thespanning element completes the expansion operation.

If there is no match—the spanning element is deemed to be irrelevant andenters a low power mode. The low power mode may also be referred to asan idle mode, a standby mode, and the like. The low power mode is termedlow power because the power consumption of an irrelevant spanningelement is lower than the power consumption of a relevant spanningelement.

In FIG. 1C various spanning elements are relevant (5061(1)-5061(3)) andone spanning element is irrelevant (5061(J)).

Each relevant spanning element may perform a spanning operation thatincludes assigning an output value that is indicative of an identity ofthe relevant spanning elements of the iteration. The output value mayalso be indicative of identities of previous relevant spanning elements(from previous iterations).

For example—assuming that spanning element number fifty is relevant andis associated with a unique set of values of eight and four—then theoutput value may reflect the numbers fifty, four and eight—for exampleone thousand multiplied by (fifty+forty) plus forty. Any other mappingfunction may be applied.

FIG. 1C also illustrates the steps executed by each spanning element:

Checking if the merge results are relevant to the spanning element (step5091).

If—so—completing the spanning operation (step 5093).

If not—entering an idle state (step 5092).

FIG. 1D is an example of various merge operations.

A merge operation may include finding regions of interest. The regionsof interest are regions within a multidimensional representation of thesensed information. A region of interest may exhibit a more significantresponse (for example a stronger, higher intensity response).

The merge operation (executed during a k'th iteration merge operation)may include at least one of the following:

Step 5031 of searching for overlaps between regions of interest (of thek'th iteration expansion operation results) and define regions ofinterest that are related to the overlaps.

Step 5032 of determining to drop one or more region of interest, anddropping according to the determination.

Step 5033 of searching for relationships between regions of interest (ofthe k'th iteration expansion operation results) and define regions ofinterest that are related to the relationship.

Step 5034 of searching for proximate regions of interest (of the k'thiteration expansion operation results) and define regions of interestthat are related to the proximity Proximate may be a distance that is acertain fraction (for example less than 1%) of the multi-dimensionalspace, may be a certain fraction of at least one of the regions ofinterest that are tested for proximity.

Step 5035 of searching for relationships between regions of interest (ofthe k'th iteration expansion operation results) and define regions ofinterest that are related to the relationship.

Step 5036 of merging and/or dropping k'th iteration regions of interestbased on shape information related to shape of the k'th iterationregions of interest.

The same merge operations may applied in different iterations.

Alternatively, different merge operations may be executed duringdifferent iterations.

FIG. 1E illustrates an example of a hybrid process and an input image6001.

The hybrid process is hybrid in the sense that some expansion and mergeoperations are executed by a convolutional neural network (CNN) and someexpansion and merge operations (denoted additional iterations ofexpansion and merge) are not executed by the CNN— but rather by aprocess that may include determining a relevancy of spanning elementsand entering irrelevant spanning elements to a low power mode.

In FIG. 1E one or more initial iterations are executed by first andsecond CNN layers 6010(1) and 6010(2) that apply first and secondfunctions 6015(1) and 6015(2).

The output of these layers provided information about image properties.The image properties may not amount to object detection. Imageproperties may include location of edges, properties of curves, and thelike.

The CNN may include additional layers (for example third till N'th layer6010(N)) that may provide a CNN output 6018 that may include objectdetection information. It should be noted that the additional layers maynot be included.

It should be noted that executing the entire signature generationprocess by a hardware CNN of fixed connectivity may have a higher powerconsumption—as the CNN will not be able to reduce the power consumptionof irrelevant nodes.

FIG. 1F illustrates a method 7000 for low-power calculation of asignature.

Method 7000 starts by step 7010 of receiving or generating a media unitof multiple objects.

Step 7010 may be followed by step 7012 of processing the media unit byperforming multiple iterations, wherein at least some of the multipleiterations comprises applying, by spanning elements of the iteration,dimension expansion process that are followed by a merge operation.

The applying of the dimension expansion process of an iteration mayinclude (a) determining a relevancy of the spanning elements of theiteration; and (b) completing the dimension expansion process byrelevant spanning elements of the iteration and reducing a powerconsumption of irrelevant spanning elements until, at least, acompletion of the applying of the dimension expansion process.

The identifiers may be retrieval information for retrieving thesignificant portions.

The at least some of the multiple iterations may be a majority of themultiple iterations.

The output of the multiple iteration may include multiple propertyattributes for each segment out of multiple segments of the media unit;and wherein the significant portions of an output of the multipleiterations may include more impactful property attributes.

The first iteration of the multiple iteration may include applying thedimension expansion process by applying different filters on the mediaunit.

The at least some of the multiple iteration exclude at least a firstiteration of the multiple iterations. See, for example, FIG. 1E.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on at least some identities of relevant spanning elementsof at least one previous iteration that preceded the iteration.

The determining the relevancy of the spanning elements of the iterationmay be based on properties of the media unit.

The determining the relevancy of the spanning elements of the iterationmay be performed by the spanning elements of the iteration.

Method 7000 may include a neural network processing operation that maybe executed by one or more layers of a neural network and does notbelong to the at least some of the multiple iterations. See, forexample, FIG. 1E.

The at least one iteration may be executed without reducing powerconsumption of irrelevant neurons of the one or more layers.

The one or more layers may output information about properties of themedia unit, wherein the information differs from a recognition of themultiple objects.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative of an identity of the relevantspanning elements of the iteration. See, for example, FIG. 1C.

The applying, by spanning elements of an iteration that differs from thefirst iteration, the dimension expansion process may include assigningoutput values that may be indicative a history of dimension expansionprocesses until the iteration that differs from the first iteration.

The each spanning element may be associated with a subset of referenceidentifiers. The determining of the relevancy of each spanning elementsof the iteration may be based a relationship between the subset of thereference identifiers of the spanning element and an output of a lastmerge operation before the iteration.

The output of a dimension expansion process of an iteration may be amultidimensional representation of the media unit that may include mediaunit regions of interest that may be associated with one or moreexpansion processes that generated the regions of interest.

The merge operation of the iteration may include selecting a subgroup ofmedia unit regions of interest based on a spatial relationship betweenthe subgroup of multidimensional regions of interest.

Method 7000 may include applying a merge function on the subgroup ofmultidimensional regions of interest. See, for example, FIG. 1C.

Method 7000 may include applying an intersection function on thesubgroup of multidimensional regions of interest. See, for example, FIG.1C.

The merge operation of the iteration may be based on an actual size ofone or more multidimensional regions of interest.

The merge operation of the iteration may be based on relationshipbetween sizes of the multidimensional regions of interest. Forexample—larger multidimensional regions of interest may be maintainedwhile smaller multidimensional regions of interest may be ignored of.

The merge operation of the iteration may be based on changes of themedia unit regions of interest during at least the iteration and one ormore previous iteration.

Step 7012 may be followed by step 7014 of determining identifiers thatare associated with significant portions of an output of the multipleiterations.

Step 7014 may be followed by step 7016 of providing a signature thatcomprises the identifiers and represents the multiple objects.

Localization and Segmentation

Any of the mentioned above signature generation method provides asignature that does not explicitly includes accurate shape information.This adds to the robustness of the signature to shape relatedinaccuracies or to other shape related parameters.

The signature includes identifiers for identifying media regions ofinterest.

Each media region of interest may represent an object (for example avehicle, a pedestrian, a road element, a human made structure,wearables, shoes, a natural element such as a tree, the sky, the sun,and the like) or a part of an object (for example—in the case of thepedestrian—a neck, a head, an arm, a leg, a thigh, a hip, a foot, anupper arm, a forearm, a wrist, and a hand). It should be noted that forobject detection purposes a part of an object may be regarded as anobject.

The exact shape of the object may be of interest.

FIG. 1G illustrates method 7002 of generating a hybrid representation ofa media unit.

Method 7002 may include a sequence of steps 7020, 7022, 7024 and 7026.

Step 7020 may include receiving or generating the media unit.

Step 7022 may include processing the media unit by performing multipleiterations, wherein at least some of the multiple iterations comprisesapplying, by spanning elements of the iteration, dimension expansionprocess that are followed by a merge operation.

Step 7024 may include selecting, based on an output of the multipleiterations, media unit regions of interest that contributed to theoutput of the multiple iterations.

Step 7026 may include providing a hybrid representation, wherein thehybrid representation may include (a) shape information regarding shapesof the media unit regions of interest, and (b) a media unit signaturethat includes identifiers that identify the media unit regions ofinterest.

Step 7024 may include selecting the media regions of interest persegment out of multiple segments of the media unit. See, for example,FIG. 2.

Step 7026 may include step 7027 of generating the shape information.

The shape information may include polygons that represent shapes thatsubstantially bound the media unit regions of interest. These polygonsmay be of a high degree.

In order to save storage space, the method may include step 7028 ofcompressing the shape information of the media unit to providecompressed shape information of the media unit.

FIG. 1H illustrates method 5002 for generating a hybrid representationof a media unit.

Method 5002 may start by step 5011 of receiving or generating a mediaunit.

Step 5011 may be followed by processing the media unit by performingmultiple iterations, wherein at least some of the multiple iterationscomprises applying, by spanning elements of the iteration, dimensionexpansion process that are followed by a merge operation.

The processing may be followed by steps 5060 and 5062.

The processing may include steps 5020, 5030, 5040 and 5050.

Step 5020 may include performing a k'th iteration expansion process (kmay be a variable that is used to track the number of iterations).

Step 5030 may include performing a k'th iteration merge process.

Step 5040 may include changing the value of k.

Step 5050 may include checking if all required iterations were done—ifso proceeding to steps 5060 and 5062. Else—jumping to step 5020.

The output of step 5020 is a k'th iteration expansion result.

The output of step 5030 is a k'th iteration merge result.

For each iteration (except the first iteration)—the merge result of theprevious iteration is an input to the current iteration expansionprocess.

Step 5060 may include completing the generation of the signature.

Step 5062 may include generating shape information regarding shapes ofmedia unit regions of interest. The signature and the shape informationprovide a hybrid representation of the media unit.

The combination of steps 5060 and 5062 amounts to a providing a hybridrepresentation, wherein the hybrid representation may include (a) shapeinformation regarding shapes of the media unit regions of interest, and(b) a media unit signature that includes identifiers that identify themedia unit regions of interest.

Object Detection Using Compressed Shape Information

Object detection may include comparing a signature of an input image tosignatures of one or more cluster structures in order to find one ormore cluster structures that include one or more matching signaturesthat match the signature of the input image.

The number of input images that are compared to the cluster structuresmay well exceed the number of signatures of the cluster structures. Forexample—thousands, tens of thousands, hundreds of thousands (and evenmore) of input signature may be compared to much less cluster structuresignatures. The ratio between the number of input images to theaggregate number of signatures of all the cluster structures may exceedten, one hundred, one thousand, and the like.

In order to save computational resources, the shape information of theinput images may be compressed.

On the other hand—the shape information of signatures that belong to thecluster structures may be uncompressed—and of higher accuracy than thoseof the compressed shape information.

When the higher quality is not required—the shape information of thecluster signature may also be compressed.

Compression of the shape information of cluster signatures may be basedon a priority of the cluster signature, a popularity of matches to thecluster signatures, and the like.

The shape information related to an input image that matches one or moreof the cluster structures may be calculated based on shape informationrelated to matching signatures.

For example—a shape information regarding a certain identifier withinthe signature of the input image may be determined based on shapeinformation related to the certain identifiers within the matchingsignatures.

Any operation on the shape information related to the certainidentifiers within the matching signatures may be applied in order todetermine the (higher accuracy) shape information of a region ofinterest of the input image identified by the certain identifier.

For example—the shapes may be virtually overlaid on each other and thepopulation per pixel may define the shape.

For example—only pixels that appear in at least a majority of theoverlaid shaped should be regarded as belonging to the region ofinterest.

Other operations may include smoothing the overlaid shapes, selectingpixels that appear in all overlaid shapes.

The compressed shape information may be ignored of or be taken intoaccount.

FIG. 1I illustrates a matching process and a generation of a higheraccuracy shape information.

It is assumed that there are multiple (M) cluster structures4974(1)-4974(M). Each cluster structure includes cluster signatures,metadata regarding the cluster signatures, and shape informationregarding the regions of interest identified by identifiers of thecluster signatures.

For example—first cluster structure 4974(1) includes multiple (N1)signatures (referred to as cluster signatures CS) CS(1,1)-CS(1,N1)4975(1,1)-4975(1,N1), metadata 4976(1), and shape information (Shapeinfo4977(1)) regarding shapes of regions of interest associated withidentifiers of the CSs.

Yet for another example—M'th cluster structure 4974(M) includes multiple(N2) signatures (referred to as cluster signatures CS) CS(M,1)-CS(M,N2)4975(M,1)-4975(M,N2), metadata 4976(M), and shape information (Shapeinfo4977(M)) regarding shapes of regions of interest associated withidentifiers of the CSs.

The number of signatures per concept structure may change over time—forexample due to cluster reduction attempts during which a CS is removedfrom the structure to provide a reduced cluster structure, the reducedstructure is checked to determine that the reduced cluster signature maystill identify objects that were associated with the (non-reduced)cluster signature—and if so the signature may be reduced from thecluster signature.

The signatures of each cluster structures are associated to each other,wherein the association may be based on similarity of signatures and/orbased on association between metadata of the signatures.

Assuming that each cluster structure is associated with a uniqueobject—then objects of a media unit may be identified by finding clusterstructures that are associated with said objects. The finding of thematching cluster structures may include comparing a signature of themedia unit to signatures of the cluster structures- and searching forone or more matching signature out of the cluster signatures.

In FIG. 1I—a media unit having a hybrid representation undergoes objectdetection. The hybrid representation includes media unit signature 4972and compressed shape information 4973.

The media unit signature 4972 is compared to the signatures of the Mcluster structures—from CS(1,1) 4975(1,1) till CS(M,N2) 4975(M,N2).

We assume that one or more cluster structures are matching clusterstructures.

Once the matching cluster structures are found the method proceeds bygenerating shape information that is of higher accuracy then thecompressed shape information.

The generation of the shape information is done per identifier.

For each j that ranges between 1 and J (J is the number of identifiersper the media unit signature 4972) the method may perform the steps of:

Find (step 4978(j)) the shape information of the j'th identifier of eachmatching signature- or of each signature of the matching clusterstructure.

Generate (step 4979(j)) a higher accuracy shape information of the j'thidentifier.

For example—assuming that the matching signatures include CS(1,1)2975(1,1), CS(2,5) 2975(2,5), CS(7,3) 2975(7,3) and CS(15,2) 2975(15,2),and that the j'th identifier is included in CS(1,1) 2975(1,1),CS(7,3)2975(7,3) and CS(15,2) 2975(15,2)—then the shape information of the j'thidentifier of the media unit is determined based on the shapeinformation associated with CS(1,1) 2975(1,1),CS(7,3) 2975(7,3) andCS(15,2) 2975(15,2).

FIG. 1P illustrates an image 8000 that includes four regions of interest8001, 8002, 8003 and 8004. The signature 8010 of image 8000 includesvarious identifiers including ID1 8011, ID2 8012, ID3 8013 and ID4 8014that identify the four regions of interest 8001, 8002, 8003 and 8004.

The shapes of the four regions of interest 8001, 8002, 8003 and 8004 arefour polygons. Accurate shape information regarding the shapes of theseregions of interest may be generated during the generation of signature8010.

FIG. 1J illustrates method 8030 for object detection.

Method 8030 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8030 may include a sequence of steps 8032, 8034, 8036 and 8038.

Step 8032 may include receiving or generating an input image.

Step 8034 may include generating a signature of the input image.

Step 8036 may include comparing the signature of the input image tosignatures of a certain concept structure. The certain concept structuremay be generated by method 8020.

Step 8038 may include determining that the input image comprises theobject when at least one of the signatures of the certain conceptstructure matches the signature of the input image.

FIG. 2D illustrates method 8040 for object detection.

Method 8040 may include the steps of method 8020 or may be preceded bysteps 8022, 8024 and 8026.

Method 8040 may include a sequence of steps 8041, 8043, 8045, 8047 and8049.

Step 8041 may include receiving or generating an input image.

Step 8043 may include generating a signature of the input image, thesignature of the input image comprises only some of the certain secondimage identifiers; wherein the input image of the second scale.

Step 8045 may include changing a scale of the input image to the firstscale to a provide an amended input image.

Step 8047 may include generating a signature of the amended input image.

Step 8049 may include verifying that the input image comprises theobject when the signature of the amended input image comprises the atleast one certain first image identifier.

Object Detection that is Robust to Angle of Acquisition

Object detection may benefit from being robust to the angle ofacquisition—to the angle between the optical axis of an image sensor anda certain part of the object. This allows the detection process to bemore reliable, use fewer different clusters (may not require multipleclusters for identifying the same object from different images).

FIG. 1K illustrates method 8120 that includes the following steps:

Step 8122 of receiving or generating images of objects taken fromdifferent angles.

Step 8124 of finding images of objects taken from different angles thatare close to each other. Close enough may be less than 1,5,10,15 and 20degrees—but the closeness may be better reflected by the reception ofsubstantially the same signature.

Step 8126 of linking between the images of similar signatures. This mayinclude searching for local similarities. The similarities are local inthe sense that they are calculated per a subset of signatures. Forexample—assuming that the similarity is determined per two images—then afirst signature may be linked to a second signature that is similar tothe first image. A third signature may be linked to the second imagebased on the similarity between the second and third signatures- andeven regardless of the relationship between the first and thirdsignatures.

Step 8126 may include generating a concept data structure that includesthe similar signatures.

This so-called local or sliding window approach, in addition to theacquisition of enough images (that will statistically provide a largeangular coverage) will enable to generate a concept structure thatinclude signatures of an object taken at multiple directions.

Signature Tailored Matching Threshold

Object detection may be implemented by (a) receiving or generatingconcept structures that include signatures of media units and relatedmetadata, (b) receiving a new media unit, generating a new media unitsignature, and (c) comparing the new media unit signature to the conceptsignatures of the concept structures.

The comparison may include comparing new media unit signatureidentifiers (identifiers of objects that appear in the new media unit)to concept signature identifiers and determining, based on a signaturematching criteria whether the new media unit signature matches a conceptsignature. If such a match is found then the new media unit is regardedas including the object associated with that concept structure.

It was found that by applying an adjustable signature matching criteria,the matching process may be highly effective and may adapt itself to thestatistics of appearance of identifiers in different scenarios. Forexample—a match may be obtained when a relatively rear but highlydistinguishing identifier appears in the new media unit signature and ina cluster signature, but a mismatch may be declared when multiple commonand slightly distinguishing identifiers appear in the new media unitsignature and in a cluster signature.

FIG. 1L illustrates method 8200 for object detection.

Method 8200 may include:

Step 8210 of receiving an input image.

Step 8212 of generating a signature of the input image.

Step 8214 of comparing the signature of the input image to signatures ofa concept structure.

Step 8216 of determining whether the signature of the input imagematches any of the signatures of the concept structure based onsignature matching criteria, wherein each signature of the conceptstructure is associated within a signature matching criterion that isdetermined based on an object detection parameter of the signature.

Step 8218 of concluding that the input image comprises an objectassociated with the concept structure based on an outcome of thedetermining.

The signature matching criteria may be a minimal number of matchingidentifiers that indicate of a match. For example—assuming a signaturethat include few tens of identifiers, the minimal number may varybetween a single identifier to all of the identifiers of the signature.

It should be noted that an input image may include multiple objects andthat an signature of the input image may match multiple clusterstructures. Method 8200 is applicable to all of the matching processes-and that the signature matching criteria may be set for each signatureof each cluster structure.

Step 8210 may be preceded by step 8202 of determining each signaturematching criterion by evaluating object detection capabilities of thesignature under different signature matching criteria.

Step 8202 may include:

Step 8203 of receiving or generating signatures of a group of testimages.

Step 8204 of calculating the object detection capability of thesignature, for each signature matching criterion of the differentsignature matching criteria.

Step 8206 of selecting the signature matching criterion based on theobject detection capabilities of the signature under the differentsignature matching criteria.

The object detection capability may reflect a percent of signatures ofthe group of test images that match the signature.

The selecting of the signature matching criterion comprises selectingthe signature matching criterion that once applied results in a percentof signatures of the group of test images that match the signature thatis closets to a predefined desired percent of signatures of the group oftest images that match the signature.

The object detection capability may reflect a significant change in thepercent of signatures of the group of test images that match thesignature. For example—assuming, that the signature matching criteria isa minimal number of matching identifiers and that changing the value ofthe minimal numbers may change the percentage of matching test images. Asubstantial change in the percentage (for example a change of more than10, 20, 30, 40 percent) may be indicative of the desired value. Thedesired value may be set before the substantial change, proximate to thesubstantial change, and the like.

For example, referring to FIG. 1I, cluster signatures CS(1,1), CS(2,5),CS(7,3) and CS(15,2) match unit signature 4972. Each of these matchesmay apply a unique signature matching criterion.

Examples of Systems

FIG. 21M illustrates an example of a system capable of executing one ormore of the mentioned above methods.

The system include various components, elements and/or units.

A component element and/or unit may be a processing circuitry may beimplemented as a central processing unit (CPU), and/or one or more otherintegrated circuits such as application-specific integrated circuits(ASICs), field programmable gate arrays (FPGAs), full-custom integratedcircuits, etc., or a combination of such integrated circuits.

Alternatively, each component element and/or unit may implemented inhardware, firmware, or software that may be executed by a processingcircuitry.

System 4900 may include sensing unit 4902, communication unit 4904,input 4911, one or more processors—such as processor 4950, and output4919. The communication unit 4904 may include the input and/or theoutput. The communication unit 4904 may communicate with anyentity—within the vehicle (for example driver device, passenger device,multimedia device), outside the vehicle (another vehicle, anothercomputerized system—such as out-of-vehicle computerized system 4820 ofFIG. 1N, another road user, another human outside the vehicle), and thelike.

Input and/or output may be any suitable communications component such asa network interface card, universal serial bus (USB) port, disk reader,modem or transceiver that may be operative to use protocols such as areknown in the art to communicate either directly, or indirectly, withother elements of the system.

Processor 4950 may include at least some out of (and thus may notinclude at least one out of):

-   -   Multiple spanning elements 4951(q).    -   Multiple merge elements 4952(r).    -   Object detector 4953.    -   Cluster manager 4954.    -   Controller 4955.    -   Selection unit 4956.    -   Object detection determination unit 4957.    -   Signature generator 4958.    -   Movement information unit 4959.    -   Identifier unit 4960.

While system 4900 includes a sensing unit 4902—is should be noted thatit may receive sensed information from other sensors and/or that thesensing unit does not belong to the system. The system may receiveinformation from one or more sensors located in the vehicle, associatedwith the vehicle, and/or located outside the vehicle.

Any method illustrated in the specification may be fully or partiallyexecuted by system 4900, and/or may be fully or partially executed byone or more other computerized system, and/or by one or morecomputerized systems—for example by task allocations betweencomputerized systems, by a cooperation (for example—exchange ofinformation, exchange of decisions, any allocation of resources,collaborative decision, and the like) between multiple computerizedsystems.

The one or more other computerized systems may be, for example,out-of-vehicle computerized system 4820 of FIG. 1N, any otherout-of-vehicle computerized system, one or more other in-vehiclesystems, a computerized device of a person within the vehicle, anycomputerized system outside the vehicle—including for example acomputerized system of another vehicle.

An example of an other in-vehicle system is denoted 4830 in FIG. 1N andis located within vehicle 4800 that drives along road 4820.

System 4900 may obtain sensed information from any type of sensors—acamera, one or more sensors implemented using any suitable imagingtechnology instead of, or in addition to, a conventional camera, aninfrared sensor, a radar, an ultrasound sensor, any electro-opticsensor, a radiography sensor, a LIDAR (light detection and ranging),telemetry ECU sensor, shock sensor, etc.

System 4900 and/or other in-vehicle system is denoted 4830 may usesupervised and/or unsupervised learning to perform any method executedby them.

The other in-vehicle system 4830 may be an autonomous driving system, anadvance driver assistance system, or may differ from an autonomousdriving system and from an advance driver assistance system.

The other in-vehicle system 4830 may include processing circuitry 210,input/output (I/O) module 220, one or more sensors 233, and database270. The processing circuitry 210 may execute any task is it assigned orprogrammed to perform in relation to any of the methods illustrate dinthe application. Alternatively—the other in-vehicle system 4830 mayinclude another module for executing (alone or with the processingcircuit) any such task. For example—the processing circuitry may executeinstructions to provide an autonomous driving manager functionality.Alternatively—another circuit or module of the in-vehicle system 4830may provide the autonomous driving manager functionality.

While the foregoing written description of the invention enables one ofordinary skill to make and use what is considered presently to be thebest mode thereof, those of ordinary skill will understand andappreciate the existence of variations, combinations, and equivalents ofthe specific embodiment, method, and examples herein. The inventionshould therefore not be limited by the above described embodiment,method, and examples, but by all embodiments and methods within thescope and spirit of the invention as claimed.

In the foregoing specification, the invention has been described withreference to specific examples of embodiments of the invention. It will,however, be evident that various modifications and changes may be madetherein without departing from the broader spirit and scope of theinvention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under”and the like in the description and in the claims, if any, are used fordescriptive purposes and not necessarily for describing permanentrelative positions. It is understood that the terms so used areinterchangeable under appropriate circumstances such that theembodiments of the invention described herein are, for example, capableof operation in other orientations than those illustrated or otherwisedescribed herein.

Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or“clear”) are used herein when referring to the rendering of a signal,status bit, or similar apparatus into its logically true or logicallyfalse state, respectively. If the logically true state is a logic levelone, the logically false state is a logic level zero. And if thelogically true state is a logic level zero, the logically false state isa logic level one.

Those skilled in the art will recognize that the boundaries betweenlogic blocks are merely illustrative and that alternative embodimentsmay merge logic blocks or circuit elements or impose an alternatedecomposition of functionality upon various logic blocks or circuitelements. Thus, it is to be understood that the architectures depictedherein are merely exemplary, and that in fact many other architecturesmay be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality iseffectively “associated” such that the desired functionality isachieved. Hence, any two components herein combined to achieve aparticular functionality may be seen as “associated with” each othersuch that the desired functionality is achieved, irrespective ofarchitectures or intermedial components. Likewise, any two components soassociated can also be viewed as being “operably connected,” or“operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundariesbetween the above described operations merely illustrative. The multipleoperations may be combined into a single operation, a single operationmay be distributed in additional operations and operations may beexecuted at least partially overlapping in time. Moreover, alternativeembodiments may include multiple instances of a particular operation,and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may beimplemented as circuitry located on a single integrated circuit orwithin the same device. Alternatively, the examples may be implementedas any number of separate integrated circuits or separate devicesinterconnected with each other in a suitable manner.

However, other modifications, variations and alternatives are alsopossible. The specifications and drawings are, accordingly, to beregarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall notbe construed as limiting the claim. The word ‘comprising’ does notexclude the presence of other elements or steps then those listed in aclaim. Furthermore, the terms “a” or “an,” as used herein, are definedas one or more than one. Also, the use of introductory phrases such as“at least one” and “one or more” in the claims should not be construedto imply that the introduction of another claim element by theindefinite articles “a” or “an” limits any particular claim containingsuch introduced claim element to inventions containing only one suchelement, even when the same claim includes the introductory phrases “oneor more” or “at least one” and indefinite articles such as “a” or “an.”The same holds true for the use of definite articles. Unless statedotherwise, terms such as “first” and “second” are used to arbitrarilydistinguish between the elements such terms describe. Thus, these termsare not necessarily intended to indicate temporal or otherprioritization of such elements. The mere fact that certain measures arerecited in mutually different claims does not indicate that acombination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated anddescribed herein, many modifications, substitutions, changes, andequivalents will now occur to those of ordinary skill in the art. It is,therefore, to be understood that the appended claims are intended tocover all such modifications and changes as fall within the true spiritof the invention.

It is appreciated that various features of the embodiments of thedisclosure which are, for clarity, described in the contexts of separateembodiments may also be provided in combination in a single embodiment.Conversely, various features of the embodiments of the disclosure whichare, for brevity, described in the context of a single embodiment mayalso be provided separately or in any suitable sub-combination.

It will be appreciated by persons skilled in the art that theembodiments of the disclosure are not limited by what has beenparticularly shown and described hereinabove. Rather the scope of theembodiments of the disclosure is defined by the appended claims andequivalents thereof.

We claim:
 1. A method for providing an in-vehicle indication, the methodcomprises: obtaining, during a driving session of a vehicle, externalaudio information about audio generated outside the vehicle; determiningwhether the audio generated outside the vehicle comprises drivingaffecting audio; and generating the in-vehicle indication within thevehicle, when determining that the audio generated outside comprisesdriving affecting audio, wherein the in-vehicle indication is indicativeabout the driving affecting audio generated outside the vehicle.
 2. Themethod according to claim 1 wherein the in-vehicle indication comprisesdriving relevant in-vehicle audio.
 3. The method according to claim 2wherein the driving relevant in-vehicle audio is a reconstructed versionof the driving affecting audio.
 4. The method according to claim 2wherein the obtaining of the external audio information comprisingsensing the external audio information by at least one external audiosensor.
 5. The method according to claim 2 comprising estimating atleast one other sound to be generated within the vehicle, when thedriving relevant in-vehicle audio is played in the vehicle, wherein theat least one other sound differ from the driving relevant in-vehicleaudio.
 6. The method according to claim 5 comprising generating thedriving relevant in-vehicle audio based on the estimating.
 7. The methodaccording to claim 5 comprising generating the driving relevantin-vehicle audio to be heard by at least the driver at the presence ofthe at least one other sound.
 8. The method according to claim 5 whereinthe at least one other sound is generated by a media device that islocated, during the driving session, within the vehicle.
 9. The methodaccording to claim 2 comprising estimating a driving mode of the vehicleto be applied by the vehicle, when the driving relevant in-vehicle audiois played in the vehicle, wherein the generating of the driving relevantin-vehicle audio is responsive to the driving mode of the vehicle,wherein the driving mode of the vehicle is selected out of multipledriving modes, the multiple driving modes comprise autonomous driving,partial autonomous driving mode and human driving mode.
 10. The methodaccording to claim 9 wherein the generating of the driving relevantin-vehicle audio comprising setting at least a volume of the drivingrelevant in-vehicle audio based on the driving mode.
 11. The methodaccording to claim 2 wherein the determining of whether the audiogenerated outside the vehicle comprises driving affecting audio isexecuted by a machine learning process that was trained to detectdriving affecting audio.
 12. The method according to claim 2 comprisingestimating whether the driving affecting audio is heard by a driver ofthe vehicle, and preventing from generating the driving relevantin-vehicle audio when the driving affecting audio is heard by the driverof the vehicle.
 13. The method according to claim 2 comprisingestimating whether the driving affecting audio is heard within thevehicle, and preventing from generating the driving relevant in-vehicleaudio when the driving affecting audio is heard in the vehicle.
 14. Anon-transitory computer readable medium that stores instructions for:obtaining, during a driving session of a vehicle, external audioinformation about audio generated outside the vehicle; determiningwhether the audio generated outside the vehicle comprises drivingaffecting audio; and generating the in-vehicle indication, within thevehicle, when determining that the audio generated outside comprisesdriving affecting audio, wherein the in-vehicle indication is indicativeabout the driving affecting audio generated outside the vehicle.
 15. Acomputerized system for providing driving relevant in-vehicle audio, thecomputerized system comprises a processor that is configured to: obtain,during a driving session of a vehicle, external audio information aboutaudio generated outside the vehicle; determine whether the audiogenerated outside the vehicle comprises driving affecting audio; andgenerate the in-vehicle indication, within the vehicle, when determiningthat the audio generated outside comprises driving affecting audio,wherein the in-vehicle indication is indicative about the drivingaffecting audio generated outside the vehicle.